code: def _pre_attention_pool(
      tensor: torch.Tensor,
      thw_shape: List[int],
  ) -> Tuple[
      torch.Tensor, Tuple[int, int, int, int, int, int, int, int]
  ]:
      """
      Apply pool to a flattened input (given pool operation and the unflattened shape).
  
  
                                           Input
                                             ↓
                                          Reshape
                                             ↓
                                            Pool
                                             ↓
                                          Reshape
                                             ↓
                                            Norm
  
  
      Args:
          tensor (torch.Tensor): Input tensor.
          pool (Optional[Callable]): Pool operation that is applied to the input tensor.
              If pool is none, return the input tensor.
          thw_shape (List): The shape of the input tensor (before flattening).
          has_cls_embed (bool): Whether the input tensor contains cls token. Pool
              operation excludes cls token.
          norm: (Optional[Callable]): Optional normalization operation applied to
           tensor after pool.
  
      Returns:
          tensor (torch.Tensor): Input tensor after pool.
          thw_shape (List[int]): Output tensor shape (before flattening).
      """
      if torch.jit.is_scripting():
          tensor, tensor_dim = _unsqueeze_dims_jit(tensor)
      else:
          tensor, tensor_dim = _unsqueeze_dims_fx(tensor)
      B, N, L, C = tensor.shape
      T, H, W = thw_shape
      tensor = (
          tensor.reshape(B * N, T, H, W, C)
          .permute(0, 4, 1, 2, 3)
          .contiguous()
      )
  
      return tensor, (B, N, L, C, T, H, W, tensor_dim)
  
imports: from typing import List, Optional, Tuple
  import numpy
  import torch
  import torch.fx
  import torch.nn
  from torch.nn.common_types import _size_3_t
  from .drop_path import DropPath
label_desc: Use image node_id 5 for calling a global function with example usage: _pre_attention_pool(tensor, thw_shape) and returns: tensor,  B,  N,  L,  C,  T,  H,  W,  tensor_dim